Adipose tissue in health and disease through the lens of its building blocks

Lenz, Michael; Arts, Ilja C. W.; Peeters, Ralf; Kok, Theo M. de; Ertaylan, Gökhan

doi:10.1101/316083

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…The main components of WAT are unilocular lipid-filled white adipocytes or fat cells, comprising 90% of WAT mass, but less than 20% to 25% of cell population count 3,4 . WAT also contains blood vessels, adipocyte precursor cells, lymph nodes and nerves of the sympathetic nervous system and immune cells 5 , which interact with white adipocytes in adipose expansion and in metabolic disease 6–9 .…”

Section: Introductionmentioning

confidence: 99%

Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks

Casero

Westerberg

Horner

et al. 2021

Preprint

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White adipose tissue (WAT) plays a central role in metabolism, and multiple diseases and genetic mutations cause its remodeling, most notably obesity, which has reached pandemic levels. WAT is present in subcutaneous (SAT) and visceral (VAT) depots, and its main components are white adipocytes. Quantitative analysis of white adipocyte size and counts is of great interest to understand physiology and disease, due to intra- and inter-depot heterogeneity, as well as better prognosis for hypertrophy than hyperplasia, and for SAT expansion than VAT expansion. H&E histology of whole depot cross-sections provides excellent approximation of cell morphology and preserves spatial information. Previous studies have been limited to window subsampling of whole slides, and cell size analysis. In this paper, we present a deep learning pipeline that can segment overlapping white adipocytes on whole slides and filter out other cells. We also propose a statistical framework based on linear models to study WAT phenotypes with three interconnected levels (body weight BW, depot weight DW and quartile cell area). Applying it to find Klf14 phenotypes in mice using 147 whole slides of WAT H&E histology, we show sexual dimorphism, and different effects between depots, heterozygous parent of origin for the KO allele and genotype (WT vs. Het). In particular, whether variables are correlated (DW vs. BW and cell area vs. DW), and statistical differences between fitted linear models. We also find significant differences between hand-traced or window subsampling datasets and whole slide analysis. Finally, we provide heatmaps of cell size for all the slides, showing substantial spatial heterogeneity and local spatial correlations.

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Section: Introductionmentioning

confidence: 99%

Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks

Casero

Westerberg

Horner

et al. 2021

Preprint

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Adipose-derived mesenchymal stem cells promote the malignant phenotype of cervical cancer

Castro-Oropeza

Vázquez-Santillán

Díaz-Gastelum

et al. 2020

Sci Rep

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Epidemiological studies indicate that obesity negatively affects the progression and treatment of cervical-uterine cancer. Recent evidence shows that a subpopulation of adipose-derived stem cells can alter cancer properties. In the present project, we described for the first time the impact of adipose-derived stem cells over the malignant behavior of cervical cancer cells. The transcriptome of cancer cells cultured in the presence of stem cells was analyzed using RNA-seq. Changes in gene expression were validated using digital-PCR. Bioinformatics tools were used to identify the main transduction pathways disrupted in cancer cells due to the presence of stem cells. In vitro and in vivo assays were conducted to validate cellular and molecular processes altered in cervical cancer cells owing to stem cells. Our results show that the expression of 95 RNAs was altered in cancer cells as a result of adipose-derived stem cells. Experimental assays indicate that stem cells provoke an increment in migration, invasion, angiogenesis, and tumorigenesis of cancer cells; however, no alterations were found in proliferation. Bioinformatics and experimental analyses demonstrated that the NF-kappa B signaling pathway is enriched in cancer cells due to the influence of adipose-derived stem cells. Interestingly, the tumor cells shift their epithelial to a mesenchymal morphology, which was reflected by the increased expression of specific mesenchymal markers. In addition, stem cells also promote a stemness phenotype in the cervical cancer cells. In conclusion, our results suggest that adipose-derived stem cells induce cervical cancer cells to acquire malignant features where NF-kappa B plays a key role.

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Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples

et al. 2019

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Background Psoriasis and atopic dermatitis are two inflammatory skin diseases with a high prevalence and a significant burden on the patients. Underlying molecular mechanisms include chronic inflammation and abnormal proliferation. However, the cell types contributing to these molecular mechanisms are much less understood. Recently, deconvolution methodologies have allowed the digital quantification of cell types in bulk tissue based on mRNA expression data from biopsies. Using these methods to study the cellular composition of the skin enables the rapid enumeration of multiple cell types, providing insight into the numerical changes of cell types associated with chronic inflammatory skin conditions. Here, we use deconvolution to enumerate the cellular composition of the skin and estimate changes related to onset, progress, and treatment of these skin diseases. Methods A novel signature matrix, i.e. DerM22, containing expression data from 22 reference cell types, is used, in combination with the CIBERSORT algorithm, to identify and quantify the cellular subsets within whole skin biopsy samples. We apply the approach to public microarray mRNA expression data from the skin layers and 648 samples from healthy subjects and patients with psoriasis or atopic dermatitis. The methodology is validated by comparison to experimental results from flow cytometry and immunohistochemistry studies, and the deconvolution of independent data from isolated cell types. Results We derived the relative abundance of cell types from healthy, lesional, and non-lesional skin and observed a marked increase in the abundance of keratinocytes and leukocytes in the lesions of both inflammatory dermatological conditions. The relative fraction of these cells varied from healthy to diseased skin and from non-lesional to lesional skin. We show that changes in the relative abundance of skin-related cell types can be used to distinguish between mild and severe cases of psoriasis and atopic dermatitis, and trace the effect of treatment. Conclusions Our analysis demonstrates the value of this new resource in interpreting skin-derived transcriptomics data by enabling the direct quantification of cell types in a skin sample and the characterization of pathological changes in tissue composition. Electronic supplementary material The online version of this article (10.1186/s12920-019-0567-7) contains supplementary material, which is available to authorized users.

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Adipose tissue in health and disease through the lens of its building blocks

Cited by 3 publications

References 42 publications

Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks

Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks

Adipose-derived mesenchymal stem cells promote the malignant phenotype of cervical cancer

Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples

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